Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment

Artificial intelligence in histopathology image analysis for cancer precision medicine

In recent years, there have been rapid advancements in the field of computational pathology. This has been enabled through the adoption of digital pathology workflows that generate digital images of histopathological slides, the publication of large data sets of these images and improvements in computing infrastructure. Objectives in computational pathology can be subdivided into two categories, first the automation of routine workflows that would otherwise be performed by pathologists and second the addition of novel capabilities. This thesis focuses on the development, application, and evaluation of methods in this second category, specifically the prediction of gene expression from pathology images and the registration of pathology images among each other. In Study I, we developed a computationally efficient cluster-based technique to perform transcriptome-wide predictions of gene expression in prostate cancer from H&E-stained whole-slide-images (WSIs). The suggested method outperforms several baseline methods and is non-inferior to single-gene CNN predictions, while reducing the computational cost with a factor of approximately 300. We included 15,586 transcripts that encode proteins in the analysis and predicted their expression with different modelling approaches from the WSIs. In a cross-validation, 6,618 of these predictions were significantly associated with the RNA-seq expression estimates with FDR-adjusted p-values <0.001. Upon validation of these 6,618 expression predictions in a held-out test set, the association could be confirmed for 5,419 (81.9%). Furthermore, we demonstrated that it is feasible to predict the prognostic cell-cycle progression score with a Spearman correlation to the RNA-seq score of 0.527 [0.357, 0.665]. The objective of Study II is the investigation of attention layers in the context of multiple-instance-learning for regression tasks, exemplified by a simulation study and gene expression prediction. We find that for gene expression prediction, the compared methods are not distinguishable regarding their performance, which indicates that attention mechanisms may not be superior to weakly supervised learning in this context. Study III describes the results of the ACROBAT 2022 WSI registration challenge, which we organised in conjunction with the MICCAI 2022 conference. Participating teams were ranked on the median 90th percentile of distances between registered and annotated target landmarks. Median 90th percentiles for eight teams that were eligible for ranking in the test set consisting of 303 WSI pairs ranged from 60.1 µm to 15,938.0 µm. The best performing method therefore has a score slightly below the median 90th percentile of distances between first and second annotator of 67.0 µm. Study IV describes the data set that we published to facilitate the ACROBAT challenge. The data set is available publicly through the Swedish National Data Service SND and consists of 4,212 WSIs from 1,153 breast cancer patients. Study V is an example of the application of WSI registration for computational pathology. In this study, we investigate the possibility to register invasive cancer annotations from H&E to KI67 WSIs and then subsequently train cancer detection models. To this end, we compare the performance of models optimised with registered annotations to the performance of models that were optimised with annotations generated for the KI67 WSIs. The data set consists of 272 female breast cancer cases, including an internal test set of 54 cases. We find that in this test set, the performance of both models is not distinguishable regarding performance, while there are small differences in model calibration

Digital Image Processing Applications

Digital image processing can refer to a wide variety of techniques, concepts, and applications of different types of processing for different purposes. This book provides examples of digital image processing applications and presents recent research on processing concepts and techniques. Chapters cover such topics as image processing in medical physics, binarization, video processing, and more

VGG19+CNN: Deep Learning-Based Lung Cancer Classification with Meta-Heuristic Feature Selection Methodology

Lung illnesses are lung-affecting illnesses that harm the respiratory mechanism. Lung cancer is one of the major causes of death in humans internationally. Advance diagnosis could optimise survivability amongst humans. This remains feasible to systematise or reinforce the radiologist for cancer prognosis. PET and CT scanned images can be used for lung cancer detection. On the whole, the CT scan exhibits importance on the whole and functions as a comprehensive operation in former cancer prognosis. Thus, to subdue specific faults in choosing the feature and optimise classification, this study employs a new revolutionary algorithm called the Accelerated Wrapper-based Binary Artificial Bee Colony algorithm (AWBABCA) for effectual feature selection and VGG19+CNN for classifying cancer phases. The morphological features will be extracted out of the pre-processed image; next, the feature or nodule related to the lung that possesses a significant impact on incurring cancer will be chosen, and for this intention, herein AWBABCA has been employed. The chosen features will be utilised for cancer classification, facilitating a great level of strength and precision. Using the lung dataset to do an experimental evaluation shows that the proposed classifier got the best accuracy, precision, recall, and f1-score

DEEP LEARNING BASED SEGMENTATION AND CLASSIFICATION FOR IMPROVED BREAST CANCER DETECTION

Breast Cancer is a leading killer of women globally. It is a serious health concern caused by calcifications or abnormal tissue growth in the breast. Doing a screening and identifying the nature of the tumor as benign or malignant is important to facilitate early intervention, which drastically decreases the mortality rate. Usually, it uses ultrasound images, since they are easily accessible to most people and have no drawbacks as such, unlike in the other most famous screening technique of mammograms where in some cases you may not get a clear scan. In this thesis, the approach to this problem is to build a stacked model which makes predictions on the basis of the shape, pattern, and spread of the tumor. To achieve this, typical steps are pre-processing of images followed by segmentation of the image and classification. For pre-processing, the proposed approach in this thesis uses histogram equalization that helps in improving the contrast of the image, making the tumor stand out from its surroundings, and making it easier for the segmentation step. Through segmentation, the approach uses UNet architecture with a ResNet backbone. The UNet architecture is made specifically for biomedical imaging. The aim of segmentation is to separate the tumor from the ultrasound image so that the classification model can make its predictions from this mask. The metric result of the F1-score for the segmentation model turned out to be 97.30%. For classification, the CNN base model is used for feature extraction from provided masks. These are then fed into a network and the predictions are done. The base CNN model used is ResNet50 and the neural network used for the output layer is a simple 8-layer network with ReLU activation in the hidden layers and softmax in the final decision-making layer. The ResNet weights are initialized from training on ImageNet. The ResNet50 returns 2048 features from each mask. These are then fed into the network for decision-making. The hidden layers of the neural network have 1024, 512, 256, 128, 64, 32, and 10 neurons respectively. The classification accuracy achieved for the proposed model was 98.61% with an F1 score of 98.41%. The detailed experimental results are presented along with comparative data

An Unsupervised Method for Suspicious Regions Detection in Mammogram Images

Over the past years many researchers proposed biomedical imaging methods for computer-aided detection and classification of suspicious regions in mammograms. Mammogram interpretation is performed by radiologists by visual inspection. The large volume of mammograms to be analyzed makes such readings labour intensive and often inaccurate. For this purpose, in this paper we propose a new unsupervised method to automatically detect suspicious regions in mammogram images. The method consists mainly of two steps: preprocessing; feature extraction and selection. Preprocessing steps allow to separate background region from the breast profile region. In greater detail, gray levels mapping transform and histogram specifications are used to enhance the visual representation of mammogram details. Then, local keypoints and descriptors such as SURF have been extracted in breast profile region. The extracted keypoints are filtered by proper parameters tuning to detect suspicious regions. The results, in terms of sensitivity and confidence interval are very encouraging